多气候区夜间通风相变蓄冷系统优化设计及控制策略研究

Optimal Design and Predictive Control of PCM-based Free Cooling System in Different Climate Zones

目前,建筑领域广泛采用被动式制冷技术以降低传统暖通空调系统的能耗。其中,人们在相变材料的自然冷却(Phase Change Material-based Free Cooling,以下简称FCPCM)系统方面开展了众多研究,因为其被认为是最有可能同时实现节能和热舒适的方案之一。然而,由于缺乏定量的参数研究和成形的控制方案,FCPCM系统的应用潜力仍不明确。本研究在考虑了不同的气候分区的基础上,以符合规范的建筑模型为应用场景,通过实验装置对FCPCM系统蓄冷装置的模型进行了验证,并用于实现FCPCM系统相关参数和控制策略的设计,之后采用遗传算法和SVM预测控制对FCPCM系统结构参数以及控制方案进行了优化,进一步对FCPCM系统的现实潜力展开了研究,以促进其在实践中的应用。本研究进一步验证了FCPCM系统在实践中实现节能的可行性,提出了基于蓄冷、放冷、通风、通风加蓄冷以及关机5种运行模式的传统控制方法,并模拟了具体的控制策略。同时也对比了以SVM为预测模型、遗传算法作为优化算法实施预测控制。经验证,该控制策略可以做到尽可能地在夜间温度较小时蓄冷从而提高系统效率,同时可以在第二天不需放冷的情况下,避免不必要的蓄冷,防止过冷并减少电力消耗。

Passive cooling technologies are now widely used in the building sector to reduce the energy consumption of conventional HVAC systems.Among them,numerous studies have been conducted on Phase Change Material-based Free Cooling(hereafter FCPCM)systems,as they are considered to be one of the most promising solutions to achieve energy savings and thermal comfort at the same time.However,the application potential of FCPCM systems is still unclear due to the lack of quantitative parametric studies and shaped control schemes.In this paper,the model of the FCPCM system cooling storage unit is validated by an experimental setup,based on the consideration of different climate zones,using a codecompliant building model as the application scenario,and is used to implement the design of FCPCM system-related parameters and control strategies,followed by the optimization of FCPCM system structural parameters as well as control schemes using genetic algorithms and SVM predictive control to further The realistic potential of FCPCM systems is investigated to facilitate their application in practice.The feasibility of FCPCM systems to achieve energy savings in practice is further verified.The study proposes conventional control methods based on five modes of operation:storage,discharge,ventilation,ventilation plus storage,and shutdown,and simulates specific control strategies.The implementation of predictive control with SVM as the predictive model and genetic algorithm as the optimization algorithm is also compared.It is verified that the control strategy can achieve as much as possible to store cold during the night time when the temperature is low and thus improve the system efficiency,and at the same time avoid unnecessary storage of cold,prevent overcooling and reduce power consumption when no cooling is needed the next day.